Heart Disease is the leading cause of death in the US, with approximately 735,000 myocardial infarction (MI) events per year. Stroke is the fifth leading cause of death in the US and the number one cause of long-term adult disability with nearly 800,000 stroke events in the US each year. The reduction of risk factors for stroke and heart disease has become a high priority, with policy efforts implementing risk reduction efforts. Novel risk factors such as inflammation and infection, including influenza like illness (ILI), or environmental exposures, such as air pollution, are known to be risk factors for MI and stroke. Moreover, ambient air pollution increases the risk of ILI, suggesting the potential for air pollution triggering an ILI event, which then subsequently triggers a stroke or MI event. Racial disparities are highly prevalent in not only the levels of air pollution, but also in the health effects of air pollution, as well as having a major role in the risk of ILI, stroke and MI. Racial and ethnic minorities are at higher risk to be exposed to high levels of air pollution, and are t higher risk for infections, stroke and MI. While the relationships between air pollution/cardiovascular disease and air pollution/ILI have been independently recognized, the combined relationship between air pollution, ILI and subsequent cardiovascular events, and how racial disparities influence this relationship has not yet been investigated. To overcome the current barriers to knowledge, this application aims to (1) determine the effect racial disparities has on the relationship ILI has on the association between air pollution and MI (2) determine the effect racial disparities has on the relationship ILI has on the association between air pollution and stroke, and (3) identify the populations at greatest risk for having an MI or stroke due to the combined effects of air pollution and ILI. Furthermore, no study has addressed these relationships in a large scale, generalizable dataset that allows for exploration of geographical, urban/rural, or differences across biologically relevant variables, such as sex and age, that could further influence these relationships. We will use two separate, large-scale administrative datasets to assess these relationships. The analyses will be conducted in the MarketScan dataset first, and then replicated in the SPARCS dataset. MarketScan is an administrative dataset with nearly 230 million de-identified patients with longitudinal information on patient demographics, including residential metropolitan statistical area (MSA) and 3-digit zipcode, and ICD-9 codes (pre-2015) or ICD-10 codes (post-2015) diagnosis and procedure codes for all inpatient and outpatient visits linked by a de-identified patient identifier code. The analyses will then be replicated in the New York Department of Health Statewide Planning and Research Cooperative System (SPARCS) dataset, a comprehensive data reporting system that collects information on hospital admissions and emergency department (ED) visits within the state of New York with detailed information on patient characteristics, demographics and diagnoses. The innovation of this idea is identifying at risk groups as defined by air pollution status and ILI as opposed to traditional risk factors.